用物理规律建模产业链动态，提升抗冲击预测能力
Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
- 引入物理符号方法刻画实体状态演化规律
- 联合学习时空协同演化拓扑与物理动态，预测精度显著提升
- 适合关注产业安全与韧性评估的研究者和决策者
产业链在国民经济可持续发展中日益重要。然而，作为典型的复杂网络，数据驱动的深度学习在描述和分析复杂网络韧性方面仍处于初级阶段，核心问题在于缺乏描述系统动态的理论框架。本文提出一种融合物理信息的神经符号方法，用于描述复杂网络的演化动态以实现韧性预测。核心思路是学习物理实体活动状态的动态，并将其融入多层时空协同演化网络，利用物理信息方法实现物理符号动态与时空协同演化拓扑的联合学习，从而预测产业链韧性。实验结果表明，该模型能获得更优性能，更准确高效地预测产业链弹性，对产业发展具有重要意义。
原文摘要 · Abstract (English)
Industrial chain plays an increasingly important role in the sustainable development of national economy. However, as a typical complex network, data-driven deep learning is still in its infancy in describing and analyzing the resilience of complex networks, and its core is the lack of a theoretical framework to describe the system dynamics. In this paper, we propose a physically informative neural symbolic approach to describe the evolutionary dynamics of complex networks for resilient prediction. The core idea is to learn the dynamics of the activity state of physical entities and integrate it into the multi-layer spatiotemporal co-evolution network, and use the physical information method to realize the joint learning of physical symbol dynamics and spatiotemporal co-evolution topology, so as to predict the industrial chain resilience. The experimental results show that the model can obtain better results and predict the elasticity of the industry chain more accurately and effectively, which has certain practical significance for the development of the industry.
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